{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multi-locus-data-distinguishes-between","title":"Multi-locus data distinguishes between population growth and multiple merger coalescents","arxiv_id":"1701.07787","date":"2018-04-19","proceeding":null,"authors":[],"abstract":"We introduce a low dimensional function of the site frequency spectrum that\nis tailor-made for distinguishing coalescent models with multiple mergers from\nKingman coalescent models with population growth, and use this function to\nconstruct a hypothesis test between these model classes. The null and\nalternative sampling distributions of the statistic are intractable, but its\nlow dimensionality renders them amenable to Monte Carlo estimation. We\nconstruct kernel density estimates of the sampling distributions based on\nsimulated data, and show that the resulting hypothesis test dramatically\nimproves on the statistical power of a current state-of-the-art method. A key\nreason for this improvement is the use of multi-locus data, in particular\naveraging observed site frequency spectra across unlinked loci to reduce\nsampling variance. We also demonstrate the robustness of our method to nuisance\nand tuning parameters. Finally we show that the same kernel density estimates\ncan be used to conduct parameter estimation, and argue that our method is\nreadily generalisable for applications in model selection, parameter inference\nand experimental design.","url_abs":"http://arxiv.org/abs/1701.07787v6","url_pdf":"http://arxiv.org/pdf/1701.07787v6.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multi-locus-data-distinguishes-between","repo_url":"https://github.com/JereKoskela/Beta-Xi-Sim","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"experimental-design","task_name":"Experimental Design"},{"task_slug":"model-selection","task_name":"Model Selection"},{"task_slug":"parameter-estimation","task_name":"parameter estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}